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AIboy996 committed Sep 9, 2023
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2 changes: 1 addition & 1 deletion docs/About/devices.md
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- `2020-202?`:Nintendo Switch 国行(已于2023破解)
- `2021-2022`:<s>Xbox One X(咸鱼来,咸鱼去,净消费100¥)</s>
- `2022-202?`:Nintendo Switch 日版(二手裸机)
- `202?-202?`*Play Station 5(谁能送我一个PS5啊)*
- `2023-202?`:Play Station 5(索尼大法好)

## 其他屏幕

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74 changes: 68 additions & 6 deletions docs/About/resume.html
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<!DOCTYPE html>
<html lang="en">
<html lang="zh">

<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Document</title>
<style type="text/css">
body {
text-align: center;
font-family: "Times New Roman"
}

a {
text-decoration: none
}

a:hover {
color: #F00
}

/* div{float:left} */
li {
margin-top: 3px;
}
</style>
<title>Yang Zhang</title>
</head>

<body>
<h1>resumé</h1>
<h2>yangzhang | 张杨</h2>
<div align="center">
<div style="width:1200px; height:350px; padding-top: 30px;">
<div style="float: left;">
<!-- 4127 × 3212 -->
<img src="/assets/images/yangzhang.jpg" width="400px" />
</div>
<div style="width:700px; height:240px; padding-left: 10px; float: left; text-align: left;">
<br/>
<h1 size="6px">Yang Zhang</h1>
<button onclick="document.getElementById('player').play()">Pronounce 张杨 in Chinese</button>
<audio id="player" src="/assets/audio/zhangyang_pronounce.mp3"></audio>
<ul type="circle">
<li>M.Sc Student in <a href="https://sds.fudan.edu.cn/">School of Data Science, Fudan University</a>(since 2023.09)</li>
<li>Freshman in <a href="https://zmiclab.github.io/">Fudan ZMIC Lab</a>, Supervised by Prof. <a href="https://zmiclab.github.io/zxh/">Xiahai Zhuang</a></li>
<li>My Email: yangzhang23 [at] m [dot] fudan [dot] edu [dot] cn</li>
<li>My Homepage: <a href="https://yangzhang.site/">yangzhang.site</a> </li>
</ul>
</div>
</div>
<hr>
<!-- 研究领域, 调整下面的height可以调整每一个模块的高度,当前模块为Fields of Research,高度为60px -->
<div style="width:1200px; height:110px; padding-top: 10px;">
<h2 style="text-align: left;">Research Interests</h2>
<p style="text-align: left;">
Machine Learning, Computer Vision, Medical Image Computing
</p>
</div>
<div style="width:1200px; height:110px; padding-top: 10px;">
<h2 style="text-align: left;">Selected Publications</h2>
<p style="text-align: left;">
...
</p>
</div>
<div style="width:1200px; height:110px; padding-top: 10px;">
<h2 style="text-align: left;">Open-source Projects</h2>
<p style="text-align: left;">
...
</p>
</div>
<footer>
<hr>
<i>Last Updated on 2023.9.9</i>
</footer>
</div>
</body>

</html>
49 changes: 26 additions & 23 deletions docs/Article/SAS/SAS_application.md
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---
tags:
- SAS
---

# SAS统计应用
## 1、描述统计
## 描述统计

### 定性变量——proc freq

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+ 峰度系数**kurtosis**
+ **qqplot**

### 1.1 means过程
### means过程

```sas
proc means data=dataname
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run;
```

### 1.2 univariate过程
### univariate过程

```sas
proc univariate data=dataname;
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run;
```

## 2、统计推断=估计+假设检验
## 统计推断(估计+假设检验)

对于一个样本:

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+ 背后的分布某个参数是否等于(小于、大于)某个值?——假设检验
+ 数据是否来自正态分布?——假设检验

### 2.1 区间估计——proc means
### 区间估计——proc means

**对置信水平的理解:**

Expand All @@ -77,7 +82,7 @@ proc means data=dataname maxdec=2 stderr clm alpha=.1;标准误、置信区间
run;
```

### 2.2 假设检验
### 假设检验

思想和反证法类似:

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r = \frac{\sum _i (X_i-\bar{X})(Y_i-\bar{Y})}{\sqrt {\sum _i (X_i-\bar{X})^2\sum _i(Y_i-\bar{Y})^2}}
$$

## 3、回归分析
## 回归分析

:happy:

### 3.0.1 回归分析框架
### 回归分析框架

#### 建模

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x系数:**当给定其他所有自变量值保持不变时**,x每增加一单位,Y**期望**增加值为系数

### 3.0.2 多元线性回归
### 多元线性回归

增加无关变量R方也会上升

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这些准则用于比较对于同一个数据,哪个模型更好

### 3.1 线性回归
### 线性回归

##### reg过程需要quit

Expand Down Expand Up @@ -469,7 +474,7 @@ forward 中`slentary=value`,默认为0.5

stepwise两个参数都可以指定,slentary,slstary,默认均为0.15

### 3.2 GLM广义线性模型
### GLM广义线性模型

响应变量$Y$,均值为$\mu_Y$

Expand All @@ -487,7 +492,7 @@ $Y\sim Normal(\mu,\sigma^2), \ g=idnetity(恒等函数)$ 即为线性回

$Y\sim Poisson(\lambda), \ g=log$ 即为poisson回归,适合响应变量是计数数据

### 3.3 logistic回归
### logistic回归

![image-20210624105331838](C:\Users\22078\AppData\Roaming\Typora\typora-user-images\image-20210624105331838.png)

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run;
```

### 3.4 probit回归
### probit回归

```sas
proc logistic data=dataname;
Expand All @@ -542,7 +547,7 @@ probit(p) = \Phi^{-1}(p)\\
其中\Phi(x)是N(0,1)的累积分布函数, \ \Phi(x)=\int_{-\infty}^xexp(-\frac{x^2}{2})dx
$$

### 3.5 genmod过程
### genmod过程

线性回归

Expand All @@ -560,11 +565,9 @@ proc genmod data=dataname;
run;
```

## 聚类分析


## 4、聚类分析

### 4.0 常用距离
### 常用距离

#### 明可夫斯基距离

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### 4.1 distance过程——求距离矩阵
### distance过程——求距离矩阵

```sas
proc distanse data=数据集 out=数据集 method=方法;
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标准化!std默认标准化方法

### 4.2 系统聚类法
### 系统聚类法

输入原始数据(欧氏距离

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run;
```

### 4.3 快速聚类法
### 快速聚类法

Kmeans

Expand All @@ -720,15 +723,15 @@ proc fastclus data=数据集
run;
```

### 4.4 标准化数据
### 标准化数据

```sas
proc stdize data=dataname out = std_data;
var x1-x6;
run;
```

### 4.5 如何确定类的个数
### 如何确定类的个数

#### 谱系图

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7 changes: 6 additions & 1 deletion docs/Article/SAS/SAS_basic.md
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# SAS的基本操作
---
tags:
- SAS
---

# SAS基础
## SAS数据集概论

### 逻辑库
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21 changes: 21 additions & 0 deletions docs/Article/software.md
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---
tags:
- misc
---

# 我用的软件
本文记录各个平台我用过的好用的软件

## Windows
### everything
装机必备搜索软件
## MacOS
### AppCleaner
Mac上好用的软件卸载工具
## iOS
### yealico
信息聚合
## Android
### tachiyomi
懂得都懂
## Linux
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2 changes: 1 addition & 1 deletion docs/index.md
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<h2>你好!</h2>

这里是[张杨](./About/about/)的个人主页,你可以查看[站内导航](./About/)快速获悉本站的结构。
这里是[张杨](./About/resume.html)的个人主页,你可以查看[站内导航](./About/)快速获悉本站的结构。

<h2>联系方式</h2>

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1 change: 1 addition & 0 deletions mkdocs.yml
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- About/links.md
- Article:
- Article/index.md
- Article/software.md
- Docker:
- Article/Docker/docker_intro.md
- Article/Docker/docker_basic.md
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